def load_data(args):
"""
Modify this to load your data and labels
"""
validation_data_params = {
"dim": (args.patch_dim, args.patch_dim, args.patch_dim),
"batch_size": 1,
"n_in_channels": args.number_input_channels,
"n_out_channels": 1,
"train_test_split": args.train_test_split,
"augment": False,
"shuffle": False,
"seed": args.random_seed
}
validation_generator = DataGenerator(False, args.data_path,
**validation_data_params)
# for batch_idx in tqdm(range(validation_generator.num_batches),
# desc="Predicting on batch"):
batch_idx = 0
imgs, msks = validation_generator.get_batch(batch_idx)
fileIDs = validation_generator.get_batch_fileIDs(batch_idx)
"""
OpenVINO uses channels first tensors (NCHWD).
TensorFlow usually does channels last (NHWDC).
So we need to transpose the axes.
"""
imgs = imgs.transpose((0, 4, 1, 2, 3))
msks = msks.transpose((0, 4, 1, 2, 3))
return imgs, msks, fileIDs
for name in model.metrics_names:
print("{} = {:.4f}".format(name, m[i]))
i += 1
save_directory = "predictions_directory"
try:
os.stat(save_directory)
except:
os.mkdir(save_directory)
print("Predicting masks")
for batch_idx in tqdm(range(validation_generator.num_batches),
desc="Predicting on batch"):
imgs, msks = validation_generator.get_batch(batch_idx)
fileIDs = validation_generator.get_batch_fileIDs(batch_idx)
preds = model.predict_on_batch(imgs)
# Save the predictions as Nifti files so that we can
# display them on a 3D viewer.
for idx in tqdm(range(preds.shape[0]), desc="Saving to Nifti file"):
img = nib.Nifti1Image(imgs[idx, :, :, :, 0], np.eye(4))
img.to_filename(
os.path.join(save_directory, "{}_img.nii.gz".format(fileIDs[idx])))
msk = nib.Nifti1Image(msks[idx, :, :, :, 0], np.eye(4))
msk.to_filename(
os.path.join(save_directory, "{}_msk.nii.gz".format(fileIDs[idx])))
for idx, name in enumerate(model.metrics_names):
print("{} = {:.4f}".format(name, m[idx]))
save_directory = "predictions_directory"
try:
os.stat(save_directory)
except:
os.mkdir(save_directory)
print("Predicting masks")
for batch_idx in tqdm(range(testing_generator.num_batches),
desc="Predicting on batch"):
imgs, msks = testing_generator.get_batch(batch_idx)
fileIDs = testing_generator.get_batch_fileIDs(batch_idx)
preds = model.predict_on_batch(imgs)
# Save the predictions as Nifti files so that we can
# display them on a 3D viewer.
for idx in tqdm(range(preds.shape[0]), desc="Saving to Nifti file"):
img = nib.Nifti1Image(imgs[idx, :, :, :, 0], np.eye(4))
img.to_filename(os.path.join(save_directory,
"{}_img.nii.gz".format(fileIDs[idx])))
msk = nib.Nifti1Image(msks[idx, :, :, :, 0], np.eye(4))
msk.to_filename(os.path.join(save_directory,
"{}_msk.nii.gz".format(fileIDs[idx])))
